Diffuse fraction correlations

Reindl, Douglas T.; Beckman, W.A.; Duffie, John A.

doi:10.1016/0038-092x(90)90060-p

Cited by 714 publications

(387 citation statements)

References 9 publications

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“…The relative humidity is less relevant in energy need calculation but in some models implemented in BES codes, it is considered for the elaboration of the external environment conditions, such as the diffuse component of the horizontal solar radiation (Reindl, Beckman and Duffie 1990) and the fictive sky temperature (Martin and Berdahl 1984). Taking into account the issues discussed before, we suggest a significant level of 1%.…”

Section: Proposed Criteria Of the Best Rank And Possible Modificationmentioning

confidence: 91%

Analysis and improvement of the representativeness of EN ISO 15927-4 reference years for building energy simulation

Pernigotto

Prada

Gasparella

et al. 2013

Journal of Building Performance Simulation

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Section: Proposed Criteria Of the Best Rank And Possible Modificationmentioning

confidence: 91%

Analysis and improvement of the representativeness of EN ISO 15927-4 reference years for building energy simulation

Pernigotto

Prada

Gasparella

et al. 2013

Journal of Building Performance Simulation

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“…Therefore, to determine the diffuse component of total PAR, we followed the methods derived by Spitters et al (1986) and Reindl et al (1990) which have been widely used in the literature (Gu et al, 1999;Jing et al, 2010;Zhang et al, 2010;Bai et al, 2012). The calculation is performed deriving the diffuse PAR radiation from the following formulation (Spitters, 1986):…”

Section: Methods To Derive Total and Diffuse Parmentioning

confidence: 99%

The effect of atmospheric aerosol particles and clouds on net ecosystem exchange in the Amazon

et al. 2014

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Abstract. Carbon cycling in the Amazon is closely linked to atmospheric processes and climate in the region as a consequence of the strong coupling between the atmosphere and biosphere. This work examines the effects of changes in net radiation due to atmospheric aerosol particles and clouds on the net ecosystem exchange (NEE) of CO 2 in the Amazon region. Some of the major environmental factors affecting the photosynthetic activity of plants, such as air temperature and relative humidity, were also examined. An algorithm for clear-sky irradiance was developed and used to determine the relative irradiance, f , which quantifies the percentage of solar radiation absorbed and scattered due to atmospheric aerosol particles and clouds. Aerosol optical depth (AOD) was calculated from irradiances measured with the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor, onboard the Terra and Aqua satellites, and was validated with ground-based AOD measurements from AERONET (Aerosol Robotic Network) sun photometers. Carbon fluxes were measured using eddy covariance technique at the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) flux towers. Two sites were studied: the Jaru Biological Reserve (RBJ), located in Rondonia, and the Cuieiras Biological Reserve at the K34 LBA tower (located in a preserved region in the central Amazon). Analysis was performed continuously from 1999 to 2009 at K34 and from 1999 to 2002 at RBJ, and includes wet, dry and transition seasons. In the Jaru Biological Reserve, a 29 % increase in carbon uptake (NEE) was observed when the AOD ranged from 0.10 to 1.5 at 550 nm. In the Cuieiras Biological Reserve, the aerosol effect on NEE was smaller, accounting for an approximate 20 % increase in NEE. High aerosol loading (AOD above 3 at 550 nm) or high cloud cover leads to reductions in solar flux and strong decreases in photosynthesis up to the point where NEE approaches zero. The observed increase in NEE is attributed to an enhancement (∼ 50 %) in the diffuse fraction of photosynthetic active radiation (PAR). The enhancement in diffuse PAR can be done through increases in aerosols and/or clouds. In the present study, it was not possible to separate these two components. Significant changes in air temperature and relative humidity resulting from changes in solar radiation fluxes under high aerosol loading were also observed at both sites. Considering the long-range transport of aerosols in the Amazon, the observed changes in NEE for these two sites may occur over large areas in the Amazon, significantly altering the carbon balance in the largest rainforest in the world.

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“…Diffuse radiation (R d ) is rarely measured, and often estimated from global radiation and the fraction of R d , which can be estimated according to clearness index (R s /R 0 ), sunshine percentage and cloud cover (Boland et al, 2008;Cotfas et al, 2014;Liu and Jordan, 1960;Reindl et al, 1990;Rivington et al, 2008). Clearness index is defined as the ratio of the daily terrestrial global radiation on a horizontal surface to the daily extraterrestrial radiation on that surface and correlated with daily diffuse fraction (Liu and Jordan, 1960).…”

Section: Estimation Of Diffuse Radiation Using Empirical Equationsmentioning

confidence: 99%

Impact of estimated solar radiation on gross primary productivity simulation in subtropical plantation in southeast China

et al. 2015

Solar Energy

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Sunshine duration is widely used to estimate solar radiation, but this estimated inherently contains some uncertainties, limiting its applications. This study investigated the impacts of the estimated solar radiation on simulated gross primary productivity (GPP), which were obtained using ecosystem models -light use efficiency model (LUE) and process-based model -Boreal Ecosystem Productivity Simulator (BEPS) at an evergreen coniferous forest ecosystem in southeast China. The models for solar radiation and diffuse radiation estimation were calibrated through observation data from nearby meteorological stations. The results showed that the established model could be successfully used to estimate solar radiation with high coefficient of determination (0.92) and low root mean square error (2.18 MJ m À2 day À1 ), but the solar radiation was overestimated when the clearness index was less than 0.15 and underestimated when it was within the range of 0.2-0.35 or greater than 0.6. The estimated solar radiation has significant influence on the diffuse radiation estimation and GPP simulation comparing with using observations. The two ecosystem models reacted differently to the errors of estimated solar radiation. For the LUE model, the estimated solar radiation led to the underestimated GPP in growing season (May-October), and overestimated GPP during non-growing season (November-April) with the bias ranged from À11% to 10% depending on the month of a year. For the BEPS model, estimated solar radiation resulted in overestimated GPP in most months with the bias ranged from À6% to 20%. The difference between the simulated GPP based on these two sources of solar radiation could be counteracted to some extent at the annual scale, especially for LUE model.

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Diffuse fraction correlations

Cited by 714 publications

References 9 publications

Analysis and improvement of the representativeness of EN ISO 15927-4 reference years for building energy simulation

Analysis and improvement of the representativeness of EN ISO 15927-4 reference years for building energy simulation

The effect of atmospheric aerosol particles and clouds on net ecosystem exchange in the Amazon

Impact of estimated solar radiation on gross primary productivity simulation in subtropical plantation in southeast China

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